COBRA Notification

• All announcements
• By author, series, or subject in COBRA
• By subject area in COBRA
• RSS Feed

Most Popular Papers

• View the most frequently downloaded papers

Institutions: Join COBRA

• Add your collection to COBRA

About COBRA

• COBRA
• About bepress

GLLAMM Manual

• Sophia Rabe-Hesketh, Graduate School of Education and Graduate Group in Biostatistics, UC Berkeley
• Anders Skrondal, Biostatistics Group, Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway
• Andrew Pickles, School of Epidemiology and Health Science and CCSR, The University of Manchester , England

Download the Paper Forward to a colleague

Abstract:

This manual describes a Stata program gllamm that can estimate Generalized Linear Latent and Mixed Models (GLLAMMs). GLLAMMs are a class of multilevel latent variable models for (multivariate) responses of mixed type including continuous responses, counts, duration/survival data, dichotomous, ordered and unordered categorical responses and rankings. The latent variables (common factors or random effects) can be assumed to be discrete or to have a multivariate normal distribution. Examples of models in this class are multilevel generalized linear models or generalized linear mixed models, multilevel factor or latent trait models, item response models, latent class models and multilevel structural equation models. The program can be downloaded from http://www.gllamm.org.

Subject Area:

Computation, Statistical Models

Suggested Citation:

Sophia Rabe-Hesketh, Anders Skrondal, and Andrew Pickles, "GLLAMM Manual" (October 2004). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 160.
http://www.bepress.com/ucbbiostat/paper160